In order to assess the quality of recommendation, we selected a set of commonly used TopN recommendation metrics namely, Precision (P@N), Recall (R@N), normalized Discounted Cumulative Gain (nDCG), and HR@N.
We set the size \( N \)  of recommendation list to 50.
The diversity of recommendation lists is measured with item coverage, Gini index, and Shannon entropy [1]}.
Item coverage computes the proportion of items that a recommender system can recommend from the entire items catalog.
Gini index and Shannon entropy are two different metrics used to measure the distributional inequality.
These measures take into account that an item is recommended to only some users, in addition to the item's distribution and to how many users it is recommended [1]}.
The entropy based measure calculates the diversity offered by an item \(i\)  for a user \(u\)  in terms of the popularity of the item among the evaluated recommenders [3]}.
